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Record W3095919836 · doi:10.18618/rep.2020.4.0030

9s-Ssi: Proposta, Análise E Modulação

2020· article· pt· W3095919836 on OpenAlexaff
Leonardo Acosta Rodrigues, Diego Brum Chaves, Felipe B. Grigoletto

Bibliographic record

VenueEletrônica de Potência · 2020
Typearticle
Languagept
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsInversa Systems (Canada)
Fundersnot available
KeywordsModulation (music)InverterComputer scienceElectrical engineeringPhysicsEngineeringVoltage

Abstract

fetched live from OpenAlex

A topologia Nine-Switch (9S) é uma interessante alternativa frente ao tradicional inversor back-to-back trifásico, pois permite a redução do número de interruptores de potência. Por outro lado, algumas aplicações dispõem de tensões CC menores que o valor mínimo requerido para síntese da tensão alternada de saída, sendo necessário o emprego de um estágio CC-CC adicional. Neste sentido, o inversor com fonte dividida ou Split-Source Inverter (SSI) agrega a elevação e inversão de tensão em um único estágio de processamento de energia, dispensando múltiplos conversores. Este artigo propõe a topologia 9S-SSI que une as características de dupla porta bidirecional trifásica conjuntamente com o estágio de elevação de tensão CC. É proposta uma estratégia de modulação PWM abordada de forma vetorial e escalar. Ambas abordagens são correlatas, contudo a primeira permite a escolha direta dos vetores de comutação enquanto que a segunda possui a vantagem da facilidade de implementação. Além disso, uma análise de perdas e o dimensionamento dos dispositivos de potência são apresentados. Resultados de simulação e experimentais são apresentados para demonstrar o bom desempenho da topologia e estratégias de modulação propostas.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.224
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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